How can we harness the value of social media when managing risk? (Part I)
The emergence of social media over the last ten years has been dramatic. The number of daily Tweets is now around 500 million. Facebook has an even larger amount of daily activity and other sites like LinkedIn also generate an enormous amount of content. These channels provide a vast amount of live, continually updating data. We are already maximising these channels for better communication with our audience but could the data be captured and stored in a way that could provide insights into market activity, trends and sentiment for the trading community?
- Political instability or unrest in a major oil producing country, or news of a specific cut in oil production in a particular region or within OPEC.
- Negative opinion can spread very quickly on social media and this in turn could affect a company’s credit worthiness.
- Weather information can have an impact on commodity prices, as the need for heat or power consumption is driven by climate in most regions.
All of these indicators are likely to be present, if perhaps buried in a sea of information, on social media ahead of any change in market price of a particular commodity, financial instrument or CDS spread.
So if we find a way to continually collect all of this data and use automated processes to; efficiently organise it, filter out the relevant content, manage different languages, different spellings and acronyms and strip out # tags or anything else which could be misleading. And lets be clear, this is no mean feat, big data requires complex algorithms and extensive language libraries in order to make any sense of it.
But let’s just say we can do this…
Credit Risk Management could benefit greatly from sentiment and insight from an organised social media data set. If this data is streamed in real-time, filtered for relevance and absorbed into an advanced topic model, it can be used to provide timely information for Credit Risk Managers to analyse and monitor particular counterparties. That could then proactively prevent the impact of future credit risk events on the portfolio. Newsfeed type data could be highly useful and give more insight into a particular counterparty than would otherwise only be available from general information gained from a previous year’s financial statement, or a current public rating agency.
Market Risk Managers and Traders need up-to-date information to assess the impact of open positions and formulate trading or hedging strategies to reduce risks inherent in those positions. Social media could provide timely insights into a particular market or commodity. If such data could be streamed, organised and presented quickly to a Trader or Risk Manager, more timely trading or hedging could take place. As news or information feeds across the trading landscape, prices will change accordingly and potential profit making or loss avoiding decisions need to be taken in advance of such changes to have maximum impact. Social media information could also contain valuable insights into market events and geographical or climate related events which will in turn, drive changes to the price of a financial instrument or commodity. Looking at the geographical location of a specific Tweet or post could provide insights into specific activity in a particular region or pricing hub.
It is clear that the relevance of big data in all areas of our lives has become very significant. The technology available to consume this data and make sense of it is becoming more mainstream and better understood. The challenge for consumers of this information is in how to organise the data in a meaningful way and then how to visualise it. How to filter out the noise to quickly get to the relevant trend or sentiment that is interesting to the daily business. In trading and risk management, the importance and use of such data is only just being understood. If technology companies can harness this data to provide risk managers more proactive risk management techniques and insights, this will bring enormous benefit and dramatic change in the future.
Part II of this blog will look at trend analysis and machine learning and also how we can utilise historical social media information to help the trading environment make better decisions.